Neural Comput - Large margin low rank tensor analysis.

Tópicos

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Resumo

We present a supervised model for tensor dimensionality reduction, which is called large margin low rank tensor analysis (LMLRTA). In contrast to traditional vector representation-based dimensionality reduction methods, LMLRTA can take any order of tensors as input. And unlike previous tensor dimensionality reduction methods, which can learn only the low-dimensional embeddings with a priori specified dimensionality, LMLRTA can automatically and jointly learn the dimensionality and the low-dimensional representations from data. Moreover, LMLRTA delivers low rank projection matrices, while it encourages data of the same class to be close and of different classes to be separated by a large margin of distance in the low-dimensional tensor space. LMLRTA can be optimized using an iterative fixed-point continuation algorithm, which is guaranteed to converge to a local optimal solution of the optimization problem. We evaluate LMLRTA on an object recognition application, where the data are represented as 2D tensors, and a face recognition application, where the data are represented as 3D tensors. Experimental results show the superiority of LMLRTA over state-of-the-art approaches.

Resumo Limpo

present supervis model tensor dimension reduct call larg margin low rank tensor analysi lmlrta contrast tradit vector representationbas dimension reduct method lmlrta can take order tensor input unlik previous tensor dimension reduct method can learn lowdimension embed priori specifi dimension lmlrta can automat joint learn dimension lowdimension represent data moreov lmlrta deliv low rank project matric encourag data class close differ class separ larg margin distanc lowdimension tensor space lmlrta can optim use iter fixedpoint continu algorithm guarante converg local optim solut optim problem evalu lmlrta object recognit applic data repres d tensor face recognit applic data repres d tensor experiment result show superior lmlrta stateoftheart approach

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